Files
agent-framework/python/packages/ag-ui
T
Eduard van Valkenburg 8ed50009c6 Python: Centralize tool result parsing in FunctionTool.invoke() (#3854)
* Centralize tool result parsing in FunctionTool.invoke()

- Add parse_result static method to FunctionTool that converts raw
  function return values to strings at invocation time
- Add result_parser parameter to FunctionTool and @tool decorator
  for custom parsing
- Remove prepare_function_call_results from all 9 consumer files
  and from the public API
- Update MCPTool to parse MCP types directly to strings via
  _parse_tool_result_from_mcp and _parse_prompt_result_from_mcp
- Change MCPTool parse_tool_results/parse_prompt_results type from
  Literal[True] | Callable | None to Callable | None
- Remove ReturnT type parameter from FunctionTool (now single
  generic ArgsT since invoke() always returns str)
- Update all subclass signatures and docstrings

Fixes #1147

* Fix test_mcp_tool_call_tool_with_meta_integration for string results

The test was still accessing result[0].additional_properties but
invoke() now returns a string, not a list of Content objects.

* Fix SIM108 lint: use binary operator for output assignment

* Fix bedrock: use FunctionTool.parse_result instead of str() fallback

str(result) turns None into literal 'None' and dicts into Python reprs
with single quotes, breaking JSON parsing. Use the shared parse_result
which handles None as '' and serializes via json.dumps.

* updated lock

* updates from feedback
8ed50009c6 ยท 2026-02-12 13:49:42 +00:00
History
..
2025-11-05 05:25:24 +00:00
2026-02-11 00:20:29 +00:00

Agent Framework AG-UI Integration

AG-UI protocol integration for Agent Framework, enabling seamless integration with AG-UI's web interface and streaming protocol.

Installation

pip install agent-framework-ag-ui

Quick Start

Server (Host an AI Agent)

from fastapi import FastAPI
from agent_framework import Agent
from agent_framework.azure import AzureOpenAIChatClient
from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint

# Create your agent
agent = Agent(
    name="my_agent",
    instructions="You are a helpful assistant.",
    client=AzureOpenAIChatClient(
        endpoint="https://your-resource.openai.azure.com/",
        deployment_name="gpt-4o-mini",
        api_key="your-api-key",
    ),
)

# Create FastAPI app and add AG-UI endpoint
app = FastAPI()
add_agent_framework_fastapi_endpoint(app, agent, "/")

# Run with: uvicorn main:app --reload

Client (Connect to an AG-UI Server)

import asyncio
from agent_framework.ag_ui import AGUIChatClient

async def main():
    async with AGUIChatClient(endpoint="http://localhost:8000/") as client:
        # Stream responses
        async for update in client.get_response("Hello!", stream=True):
            for content in update.contents:
                if content.type == "text" and content.text:
                    print(content.text, end="", flush=True)
        print()

asyncio.run(main())

The AGUIChatClient supports:

  • Streaming and non-streaming responses
  • Hybrid tool execution (client-side + server-side tools)
  • Automatic thread management for conversation continuity
  • Integration with Agent for client-side history management

Documentation

  • Getting Started Tutorial - Step-by-step guide to building AG-UI servers and clients
    • Server setup with FastAPI
    • Client examples using AGUIChatClient
    • Hybrid tool execution (client-side + server-side)
    • Thread management and conversation continuity
  • Examples - Complete examples for AG-UI features

Features

This integration supports all 7 AG-UI features:

  1. Agentic Chat: Basic streaming chat with tool calling support
  2. Backend Tool Rendering: Tools executed on backend with results streamed to client
  3. Human in the Loop: Function approval requests for user confirmation before tool execution
  4. Agentic Generative UI: Async tools for long-running operations with progress updates
  5. Tool-based Generative UI: Custom UI components rendered on frontend based on tool calls
  6. Shared State: Bidirectional state sync between client and server
  7. Predictive State Updates: Stream tool arguments as optimistic state updates during execution

Security: Authentication & Authorization

The AG-UI endpoint does not enforce authentication by default. For production deployments, you should add authentication using FastAPI's dependency injection system via the dependencies parameter.

API Key Authentication Example

import os
from fastapi import Depends, FastAPI, HTTPException, Security
from fastapi.security import APIKeyHeader
from agent_framework import Agent
from agent_framework.ag_ui import add_agent_framework_fastapi_endpoint

# Configure API key authentication
API_KEY_HEADER = APIKeyHeader(name="X-API-Key", auto_error=False)
EXPECTED_API_KEY = os.environ.get("AG_UI_API_KEY")

async def verify_api_key(api_key: str | None = Security(API_KEY_HEADER)) -> None:
    """Verify the API key provided in the request header."""
    if not api_key or api_key != EXPECTED_API_KEY:
        raise HTTPException(status_code=401, detail="Invalid or missing API key")

# Create agent and app
agent = Agent(name="my_agent", instructions="...", client=...)
app = FastAPI()

# Register endpoint WITH authentication
add_agent_framework_fastapi_endpoint(
    app,
    agent,
    "/",
    dependencies=[Depends(verify_api_key)],  # Authentication enforced here
)

Other Authentication Options

The dependencies parameter accepts any FastAPI dependency, enabling integration with:

  • OAuth 2.0 / OpenID Connect - Use fastapi.security.OAuth2PasswordBearer
  • JWT Tokens - Validate tokens with libraries like python-jose
  • Azure AD / Entra ID - Use azure-identity for Microsoft identity platform
  • Rate Limiting - Add request throttling dependencies
  • Custom Authentication - Implement your organization's auth requirements

For a complete authentication example, see getting_started/server.py.

Architecture

The package uses a clean, orchestrator-based architecture:

  • AgentFrameworkAgent: Lightweight wrapper that delegates to orchestrators
  • Orchestrators: Handle different execution flows (default, human-in-the-loop, etc.)
  • Confirmation Strategies: Domain-specific confirmation messages (extensible)
  • AgentFrameworkEventBridge: Converts Agent Framework events to AG-UI events
  • Message Adapters: Bidirectional conversion between AG-UI and Agent Framework message formats
  • FastAPI Endpoint: Streaming HTTP endpoint with Server-Sent Events (SSE)

Next Steps

  1. New to AG-UI? Start with the Getting Started Tutorial
  2. Want to see examples? Check out the Examples for AG-UI features

License

MIT